'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


_UpperCamelCase					:		int							      =		logging.get_logger(__name__)

_UpperCamelCase					:		Union[str, Any]							      =		{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}

class 	_lowercase(  a_					):



   """simple docstring"""





   __lowerCamelCase     =	'''perceiver'''





   def __init__(				self:     int      ,a:     Dict=256      ,a:     Optional[int]=1280      ,a:     Optional[int]=768      ,a:     Dict=1      ,a:     Optional[Any]=26      ,a:     List[Any]=8      ,a:     Union[str, Any]=8      ,a:     Dict=None      ,a:     Tuple=None      ,a:     str="kv"      ,a:     Optional[int]=1      ,a:     List[Any]=1      ,a:     Tuple="gelu"      ,a:     Optional[Any]=0.1      ,a:     Optional[Any]=0.02      ,a:     Union[str, Any]=1e-12      ,a:     Any=True      ,a:     List[str]=262      ,a:     str=2048      ,a:     Optional[Any]=56      ,a:     Union[str, Any]=[368, 496]      ,a:     List[Any]=16      ,a:     List[Any]=1920      ,a:     List[Any]=16      ,a:     Tuple=[1, 16, 224, 224]      ,**a:     Any      ,):
    super().__init__(**UpperCamelCase_						)

    __UpperCAmelCase								=       num_latents
    __UpperCAmelCase								=       d_latents
    __UpperCAmelCase								=       d_model
    __UpperCAmelCase								=       num_blocks
    __UpperCAmelCase								=       num_self_attends_per_block
    __UpperCAmelCase								=       num_self_attention_heads
    __UpperCAmelCase								=       num_cross_attention_heads
    __UpperCAmelCase								=       qk_channels
    __UpperCAmelCase								=       v_channels
    __UpperCAmelCase								=       cross_attention_shape_for_attention
    __UpperCAmelCase								=       self_attention_widening_factor
    __UpperCAmelCase								=       cross_attention_widening_factor
    __UpperCAmelCase								=       hidden_act
    __UpperCAmelCase								=       attention_probs_dropout_prob
    __UpperCAmelCase								=       initializer_range
    __UpperCAmelCase								=       layer_norm_eps
    __UpperCAmelCase								=       use_query_residual
    # masked language modeling attributes
    __UpperCAmelCase								=       vocab_size
    __UpperCAmelCase								=       max_position_embeddings
    # image classification attributes
    __UpperCAmelCase								=       image_size
    # flow attributes
    __UpperCAmelCase								=       train_size
    # multimodal autoencoding attributes
    __UpperCAmelCase								=       num_frames
    __UpperCAmelCase								=       audio_samples_per_frame
    __UpperCAmelCase								=       samples_per_patch
    __UpperCAmelCase								=       output_shape




class 	_lowercase(  a_					):



   """simple docstring"""





   @property
   def       snake_case						(				self:     str						):

    if self.task == "multiple-choice":
     __UpperCAmelCase								=       {0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
     __UpperCAmelCase								=       {0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('inputs', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]						)





   @property
   def       snake_case						(				self:     Union[str, Any]						):
    return 1e-4





   def       snake_case						(				self:     Tuple      ,a:     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,a:     int = -1      ,a:     int = -1      ,a:     int = -1      ,a:     bool = False      ,a:     Optional[TensorType] = None      ,a:     int = 3      ,a:     int = 40      ,a:     int = 40      ,):

    if isinstance(UpperCamelCase_      ,UpperCamelCase_						):
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     __UpperCAmelCase								=       compute_effective_axis_dimension(
         UpperCamelCase_      ,fixed_dimension=OnnxConfig.default_fixed_batch      ,num_token_to_add=0						)
     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     __UpperCAmelCase								=       preprocessor.num_special_tokens_to_add(UpperCamelCase_						)
     __UpperCAmelCase								=       compute_effective_axis_dimension(
         UpperCamelCase_      ,fixed_dimension=OnnxConfig.default_fixed_sequence      ,num_token_to_add=UpperCamelCase_						)
     # Generate dummy inputs according to compute batch and sequence
     __UpperCAmelCase								=       [' '.join(['a']						) * seq_length] * batch_size
     __UpperCAmelCase								=       dict(preprocessor(UpperCamelCase_      ,return_tensors=UpperCamelCase_						)						)
     __UpperCAmelCase								=       inputs.pop('input_ids'						)
     return inputs
    elif isinstance(UpperCamelCase_      ,UpperCamelCase_						) and preprocessor.model_input_names[0] == "pixel_values":
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     __UpperCAmelCase								=       compute_effective_axis_dimension(UpperCamelCase_      ,fixed_dimension=OnnxConfig.default_fixed_batch						)
     __UpperCAmelCase								=       self._generate_dummy_images(UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_						)
     __UpperCAmelCase								=       dict(preprocessor(images=UpperCamelCase_      ,return_tensors=UpperCamelCase_						)						)
     __UpperCAmelCase								=       inputs.pop('pixel_values'						)
     return inputs
    else:
     raise ValueError(
         'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'						)











"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


snake_case : Optional[Any]     		=		logging.get_logger(__name__)

snake_case : str     		=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case : List[Any]     		=		{
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

snake_case : Tuple     		=		{"""mobilebert-uncased""": 5_1_2}


snake_case : List[str]     		=		{}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = MobileBertTokenizer







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	Optional[int]=None				,    UpperCamelCase_ :	str=None				,    UpperCamelCase_ :	Tuple=True				,    UpperCamelCase_ :	Any="[UNK]"				,    UpperCamelCase_ :	str="[SEP]"				,    UpperCamelCase_ :	Any="[PAD]"				,    UpperCamelCase_ :	Dict="[CLS]"				,    UpperCamelCase_ :	str="[MASK]"				,    UpperCamelCase_ :	Union[str, Any]=True				,    UpperCamelCase_ :	Dict=None				,    **UpperCamelCase_ :	Dict				,    ):





           '''simple docstring'''

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    tokenize_chinese_chars=UpperCamelCase_				,    strip_accents=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('lowercase'				,    UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('strip_accents'				,    UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('handle_chinese_chars'				,    UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                  __magic_name__      				=    getattr(UpperCamelCase_				,    normalizer_state.pop('type' ) )
                  __magic_name__      				=    do_lower_case
                  __magic_name__      				=    strip_accents
                  __magic_name__      				=    tokenize_chinese_chars
                  __magic_name__      				=    normalizer_class(**UpperCamelCase_ )

           __magic_name__      				=    do_lower_case







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	List[str]				,    UpperCamelCase_ :	List[Any]=None ):





           '''simple docstring'''

           __magic_name__      				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           __magic_name__      				=    self._tokenizer.model.save(UpperCamelCase_				,    name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )